Marks, Dembski, and Ewert open Chapter 3 by stating the central fallacy of evolutionary informatics: “Evolution is often modeled by as [sic] a search process.” The long and the short of it is that they do not understand the models, and consequently mistake what a modeler does for what an engineer might do when searching for a solution to a given problem. What I hope to convey in this post, primarily by means of graphics, is that fine-tuning a model of evolution, and thereby obtaining an evolutionary process in which a maximally fit individual emerges rapidly, is nothing like informing evolution to search for the best solution to a problem. We consider, specifically, a simulation model presented by Christian apologist David Glass in a paper challenging evolutionary gradualism à la Dawkins. The behavior on exhibit below is qualitatively similar to that of various biological models of evolution.
Animation 1. Parental populations in the first 2000 generations of a run of the Glass model, with parameters (mutation rate .005, population size 500) tuned to speed the first occurrence of maximum fitness (1857 generations, on average), are shown in orange. Offspring are generated in pairs by recombination and mutation of heritable traits of randomly mated parents. The fitness of an individual in the parental population is, loosely, the number of pairs of offspring it is expected to leave. In each generation, the parental population is replaced by surviving offspring. Which of the offspring die is arbitrary. When the model is modified to begin with a maximally fit population, the long-term regime of the resulting process (blue) is the same as for the original process. Rather than seek out maximum fitness, the two evolutionary processes settle into statistical equilibrium.
Evolution is often presented as problem-solving. Genetic algorithms are often offered as proofs of evolution’s ability to solve problems. Genetic algorithms are as search algorithms.
As one book says:
Fundamentally, all evolutionary algorithms can be viewed as search algorithms which search through a set of possible solutions looking for the best – or “fittest” – solution.
Tom has asked me to specify a problem independently from the evolutionary process. Now I have to admit that I don’t really understand what that means. But I like Tom and I have a lot of respect for him, so I want to give it my best shot and see where it takes us. I’m also hoping this will shed some light on claims about how problem-solving genetic algorithms are designed to solve a particular problem.
Recently, we have been able to establish, reluctantly by some and without an official admission, that God could not have spared Adam and Eve from the consequences of their disobedience that led to sin, which resulted in aging, diseases, suffering, natural disasters outside of paradise and then eventually death… Continue reading →
The book may make some “skeptics” uncomfortable, but maybe they should read it anyways.
From the book:
I have come to believe that there is something presently wrong with how we scientists think about life, its existence, its origins, and its evolution.
Without a coherent theory of life, whatever we think about life doesn’t hold water. This applies to the major contribution we claim that the modern science of life offers to the popular culture: Darwinism.
… there sits at the heart of modern Darwinism an unresolved tautology that undermines its validity.
… do we have a coherent theory of evolution? The firmly settled answer to this question is supposed to be “yes” …
I intend to argue in this book that the answer to my question might actually be “no.”
There’s a rather good article recently published on Aeon, “Why religion is not going away and science will not destroy it“. Since we often circle around the question of the relationship between science and religion, and since most TSZ contributors seem to assume the conflict thesis — that science and religion tend to, and perhaps even must, conflict — I wanted to bring this article to your attention. Discuss — or not!
Coevolutionary algorithms approach problems for which no function for evaluating potential solutions is present or known. Instead, algorithms rely on the aggregation of outcomes from interactions among evolving entities in order to make selection decisions. Given the lack of an explicit yardstick, understanding the dynamics of coevolutionary algorithms, judging whether a given algorithm is progressing, and designing effective new algorithms present unique challenges unlike those faced by optimization or evolutionary algorithms. The purpose of this chapter is to provide a foundational understanding of coevolutionary algorithms and to highlight critical theoretical and empirical work done over the last two decades. This chapter outlines the ends and means of coevolutionary algorithms: what they are meant to find, and how they should find it.
Handbook of Natural Computing
Until you have an eye there is nothing to select for. You have 300k of nucleotides drifting toward a meaningless group of sequences. Until you find a group of sequences that can provide reproductive advantage (sight) it is drift drift drift.
This is just a version of the “what good is half an eye” PRATT.
Seriously, Bill, how can you possibly have missed everything that’s been written on this subject, from Darwin onward?
One of the deeper questions that runs throughout philosophical speculation — Western, Eastern, and besides — is a kind of wonder or awe at the fact that the world does make any sense to us all. This awe can be expressed as itself an intellectual problem: why is the world intelligible? The question is sometimes put as: what is the source of the world’s intelligibility? Is the source of intelligibility itself intelligible? Or does a mystery remain after all explanations have had their say?